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Evaluating single molecule detection methods for microarrays with high dynamic range for quantitative single cell analysis.

Abstract：

:Single molecule microarrays have been used in quantitative proteomics, in particular, single cell analysis requiring high sensitivity and ultra-low limits of detection. In this paper, several image analysis methods are evaluated for their ability to accurately enumerate single molecules bound to a microarray spot. Crucially, protein abundance in single cells can vary significantly and may span several orders of magnitude. This poses a challenge to single molecule image analysis. In order to quantitatively assess the performance of each method, synthetic image datasets are generated with known ground truth whereby the number of single molecules varies over 5 orders of magnitude with a range of signal to noise ratios. Experiments were performed on synthetic datasets whereby the number of single molecules per spot corresponds to realistic single cell distributions whose ground truth summary statistics are known. The methods of image analysis are assessed in their ability to accurately estimate the distribution parameters. It is shown that super-resolution image analysis methods can significantly improve counting accuracy and better cope with single molecule congestion. The results highlight the challenge posed by quantitative single cell analysis and the implications to performing such analyses using microarray based approaches are discussed.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Salehi-Reyhani A

doi

10.1038/s41598-017-18303-z

subject

Has Abstract

pub_date

2017-12-20 00:00:00

pages

17957

issue

1

issn

2045-2322

pii

10.1038/s41598-017-18303-z

journal_volume

7

pub_type

杂志文章

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